class_docstr: Fuse aggregated embeddings from both incoming and outgoing
  directions before updating node embeddings.
  .. math::
     h_{i}^{0} = [ x_i \| \mathbf{0} ]
     a_{i, \vdash}^{t} = \sum_{j\in\mathcal{N}_{\vdash }(i)}
     W_{\vdash} h_{j}^{t}
     a_{i, \dashv}^{t} = \sum_{j\in\mathcal{N}_{\dashv }(i)}
     W_{\dashv} h_{j}^{t}
     e_{i}^{t} &= \sigma (W_{f}[a_{i, \vdash}^{t};a_{i, \dashv}^{t};
     a_{i, \vdash}^{t}*a_{i, \dashv}^{t};
     a_{i, \vdash}^{t}-a_{i, \dashv}^{t}])
     h_{i}^{t+1} = \mathrm{GRU}(e_{i}^{t}, h_{i}^{t})
  Attributes
  ----------
  input_size: int
      Input feature size.
  output_size: int
      Output feature size.
  n_etypes: int
      Number of edge types. Default: 1.
  bias: bool
      If True, adds a learnable bias to the output. Default: True.
  Attributes
  ----------
  input_size: int
      Input feature size.
  output_size: int
      Output feature size.
  n_etypes: int
      Number of edge types. Default: 1.
  bias: bool
      If True, adds a learnable bias to the output. Default: True.
code: def __init__(self, input_size, output_size, n_etypes=1, bias=True):
      super(BiFuseGGNNLayerConv, self).__init__()
      self._input_size = input_size
      self._output_size = output_size
      # self._num_layers = num_layers
      self._n_etypes = n_etypes
  
      self.linears_in = nn.ModuleList(
          [nn.Linear(output_size, output_size) for _ in range(n_etypes)]
      )
  
      self.linears_out = nn.ModuleList(
          [nn.Linear(output_size, output_size) for _ in range(n_etypes)]
      )
  
      self.gru = nn.GRUCell(output_size, output_size, bias=bias)
      self.fuse_linear = nn.Linear(
          4 * output_size, output_size, bias=True
      )
      self.reset_parameters()
  
imports: import dgl
  import dgl.function
  import torch
  import torch.nn
  from torch.nn import init
  from ...data.data import GraphData
  from .base import GNNBase, GNNLayerBase
label_desc: Use image node_id 1 to create a new BiFuseGGNNLayerConv object from inherited base classes: GNNLayerBase with example: obj = BiFuseGGNNLayerConv(input_size, output_size, n_etypes, bias)